Molecular Representation Learning

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Molecular representation learning studies how to encode molecules and biomolecules so models can recognize chemical structure, predict properties, and support tasks such as drug screening and molecular design. Approaches range from fingerprints and molecular graphs to SMILES language models, 3D equivariant networks, fragment-based methods, and multimodal models combining structure with spectra, text, or biological measurements. Self-supervised and contrastive pretraining are common ways to learn from unlabeled structures, while supervised models use task-specific labels for properties such as solubility, binding affinity, or toxicity. A central challenge is reliable transfer to new chemical scaffolds and biological settings: larger pretrained models do not always beat simpler fingerprints or descriptors, and results depend strongly on data splits, representation choices, and whether physical and chemical information is preserved.

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207 papers28 papers in the last four weeks, up 56% on the four weeks before. 0.2% of all new papers.

A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis
Jun 18, 2026Chen Yang, Zheng Fang, Hanyu Sun +7Synthetic Data Pretraining Synthetic-to-Real Domain Adaptation
The Hong Kong University of Science and Technology (Guangzhou) · Peking Union Medical College and Chinese Academy of Medical Sciences · Xiamen University +3
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
Jun 16, 2026Yingxu Wang, Kunyu Zhang, Nan Yin +2Molecular Property Prediction Learning with Noisy Labels
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, AI Diyafah St, 7909, Abu Dhabi, United Arab Emirates · Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China · International College, Zhengzhou University, Daxue North Road, 450000, Henan, China +2
Latent space mapping of interpretable structural coordinates from stochastic single-molecule signals
Jun 15, 2026Matteo Cartiglia, Sandro Kuppel, Wouter Botermans Wannes Peeters +7Molecular Representation Learning
imec, Kapeldreef 75, 3001 Heverlee, Belgium
Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction
Jun 12, 2026Peng-Fei Sun, Chuan-Xian Ren, Hong YanProtein-Ligand Binding Affinity Prediction Graph Neural Networks
School of Mathematics, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China · Department of Electrical Engineering, City University of Hong Kong, Hong Kong
Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
Jun 12, 2026Mohamed Mouhajir, Limei Wang, El Houcine Bergou +3Graph Neural Networks Protein Representation Learning
College of Computing, UM6P Morocco · Independent USA
Smoothing Dark Areas in Molecular Latent Diffusion
Jun 11, 2026Xi Wang, Jiahan Li, Yuxuan Xia +3Variational Autoencoders Latent Diffusion Models
New York University · Stanford University
CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context
Jun 10, 2026Wei Zhang, Xun Jiang, Yuesi Xi +1Single-Cell Perturbation Modeling Genomics
1L3S Research Center, Leibniz Universität Hannover, Germany · School of Clinical Medicine & Laboratory Medicine, Jiangsu University, China · Institute for Information Processing (tnt), Leibniz Universität Hannover, Germany.
Viral Proteins Reveal Geometry of Protein Language Models
Jun 10, 2026Arthur Bigot, Harmon Bhasin, Core Francisco Park +2Protein Language Models Protein Representation Learning
Department of Computer Science, ETH Zurich, Zurich, Switzerland · Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA · TwentyTwo, USA +2
GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction
Jun 9, 2026Emily Nguyen, Yongchan Hong, Harsh Toshniwal +2Multimodal Pretraining Molecular Property Prediction
Department of Medical Biochemistry and Biophysics Science for Life Laboratory Karolinska Institutet Stockholm, Sweden · Amazon
Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction
Jun 8, 2026Limin YuMachine Learning Interatomic Potentials Multi-Scale Feature Fusion
School of Medical Imaging, Division of Medical Technology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300203, China
A systematic investigation of molecular encoding methods for drug property predictions across neural network and Transformer encoder-based model
Jun 8, 2026Sheng-Ya Chen, Shan-Ju YehMolecular Property Prediction Molecular Representation Learning
Department of Life Science, National Tsing Hua University, Hsinchu, Taiwan. · 4Interdisciplinary Program of Life Sciences and Medicine, National Tsing Hua University, Hsinchu, Taiwan. · School of Medicine, National Tsing Hua University, Hsinchu, Taiwan. +1
The Dark Regulome: Disentangling Predictability from Regulation in Genomic Foundation Models
Jun 5, 2026Chahat Baranwal, Aaditya Baranwal, Lakshya Nitin TandonGenomic Foundation Models Genomics
IIT Jodhpur · University of Central Florida · Northeastern University
LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling
Jun 3, 2026Daria Ledneva, Denis KuznetsovAdaptive Tokenization Genomic Foundation Models
Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia.
GENEB: Why Genomic Models Are Hard to Compare
Jun 3, 2026Daria Ledneva, Mikhail Nuridinov, Denis KuznetsovBenchmark Design Genomic Foundation Models
Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia · Moscow State Institute of Steel and Alloys, Moscow, Russia
Learning Topological Representations of Protein Structure and Dynamics
Jun 2, 2026Dominik Geng, Florian Graf, Martin Uray +1Generative Modeling Persistent Homology
University of Salzburg, Austria · Josef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria
Rethinking Molecular Text Representations for LLMs: An Empirical Study
Jun 2, 2026Arun Raja, Garrett M. Morris, Kian Ming A. ChaiLLM Evaluation Molecular Generation
University of Oxford · DSO National Laboratories
CryoProt: A Protein Pretraining Framework with Cross-Box Interactions on Cryo-EM Density Maps
May 31, 2026Dan Luo, Xuan Lin, Peng Zhou +4Cryo-Electron Microscopy 3D Representation Learning
College of Computer Science and Electronic Engineering, Hunan University · School of Computer Science, Xiangtan University
Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding
May 30, 2026Zaifei Yang, Samuel Ping-Man Choi, James KwokHierarchical Representation Learning Multimodal Embedding
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. · Lee Shau Kee School of Business and Administration, The Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong.
Effective Biological Representation Learning by Masking Gene Expression
May 29, 2026Kian Kenyon-Dean, Alina Selega, Ihab Bendidi +5Masked Autoencoders Self-Supervised Learning
Recursion · Valence Labs · École Normale Supérieure PSL
Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model
May 29, 2026Fengtao Zhou, Yingxue Xu, Zhengyu Zhang +20Spatial Transcriptomics Cross-Modal Alignment
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +16
Chem-PerturBridge: a harmonized compendium of small molecule perturbation transcriptomic effects
May 29, 2026Artur Szałata, Olga Novitskaia, Maiia Shulman +3Cross-Dataset Generalization Molecular Representation Learning
Institute of Computational Biology, Helmholtz Center Munich · TUM School of Life Sciences Weihenstephan, Technical University of Munich · Institut Curie, INSERM U1331, Computational Oncology +1
The Geometry of Activity Cliffs: Representation Dependence and Multi-Scale Characterization of Activity Landscapes
May 29, 2026Pawel Dabrowski-Tumanski, Bartosz Topolski, Dariusz Plewczynski +1Representation Geometry Molecular Representation Learning
†Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw 00-662, Poland · ¶Ingenix.AI · §Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland
OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction
May 28, 2026Xin Wang, Linxin Xiao, Yang Yao +1OOD Generalization Multimodal Graph Learning
DCST, BNRist, Tsinghua University · Beijing, China · DCST, Tsinghua University
What drives performance in molecular MPNNs? An operator-level factorial benchmark
May 28, 2026Panyu Jiao, Shuizhou Chen, Yiheng Shen +3Message Passing Neural Networks Molecular Property Prediction
Materials Genome Institute, Shanghai University, Shanghai 200444, China · School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China · School of Materials Science and Engineering, Tongji University, Shanghai 201804, China
A Triple-Modal Contrastive Learning Framework with Sequence, Graph, and 3D Features for Drug-Target Interaction Prediction
May 28, 2026Le Xu, Xi Zhang, Dan Luo +2Multimodal Contrastive Learning Molecular Representation Learning
School of Computer Science, Xiangtan University, Xiangtan 411105, China
From Detection to Mechanism: Cross-Attention Graph Neural Networks Enable Drug-Drug Interaction Type Prediction An Ablation Study with Acetylsalicylic Acid Validation
May 27, 2026Juergen DietrichGraph Neural Networks Drug-Drug Interaction Prediction
SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception
May 26, 2026Ziqi Zhang, Eunyeong Jin, Miguel Vasco +6Cross-Modal Alignment Molecular Property Prediction
1Dept. of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden · 2Atmospheric Chemistry Dept., Max Planck Institute for Chemistry, Mainz, Germany · 3Dept. of Information Technology, Uppsala University, Uppsala, Sweden +1
Periodic Topological Deep Learning for Polymer Design and Discovery
May 26, 2026Yasharth Yadav, Tze Kwang Gerald Er, Atsushi Goto +1Materials Property Prediction Polymer Property Prediction
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371 · School of Chemistry, Chemical Engineering and Biotechnology (CCEB), Nanyang Technological University, Singapore 637371
SurfDesign: Effective Protein Design on Molecular Surfaces
May 25, 2026Fang Wu, Shuting Jin, Xiangru Tang +5Protein Design Molecular Representation Learning
Stanford University Stanford, USA · Wuhan University of Science and Technology Wuhan, China · Yale University New Haven, USA +5
ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks
May 25, 2026Dongxin Ye, Fang Hu, Han Hu +6Distribution Shift Benchmark Design
Shanghai Innovation Institute Shanghai, China · University of Electronic Science and Technology of China Chengdu, China · Fudan University Shanghai, China +7

Jun 18, 2026J/K move · Enter open · S save

A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis

Chen Yang, Zheng Fang, Hanyu Sun, Fanjie Xu+6

The Hong Kong University of Science and Technology (Guangzhou) · Peking Union Medical College and Chinese Academy of Medical Sciences · Xiamen University+3

Synthetic Data Pretraining Synthetic-to-Real Domain Adaptation Molecular Representation Learning Magnetic Resonance Spectroscopy

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Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for molecular structure analysis, and spectral artificial intelligence offers great potential for its rapid and automated interpretation. However, the scarcity of experimental NMR datasets has constrained deep learning in this domain to narrow, task-specific applications that lack broad generalization. Here, we introduce UltraNMR, a large-scale foundation model for NMR that leverages the intrinsic properties of NMR spectra to learn generalizable spectral representations. We collected 158 million paired simulated

^{1}

H and

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C NMR spectra to train UltraNMR, employing multiple domain-specific pre-training objectives. UltraNMR captures both intra-spectral and inter-spectral dependencies, enabling seamless simulation-to-real adaptation. We demonstrate that adapting UltraNMR to a range of molecular structure analysis tasks on experimental NMR spectra consistently yields state-of-the-art performance and clearly outperforms UltraNMR variants trained directly on downstream data without simulation pre-training. We also construct a large-scale NMR spectral vector library by encoding simulated NMR spectra using UltraNMR, covering 94 million unique molecules and enabling effective structure-aware retrieval. In real-world applications, UltraNMR facilitates the structural elucidation of two previously unknown natural products from Chinese herbal medicines recorded in the Chinese Pharmacopoeia. These results suggest that large-scale simulation pre-training can effectively bridge the simulation-to-real gap, enabling robust and generalizable molecular structure analysis of real-world NMR spectra.